Modern inventory systems, such as those in mail order warehouses, supply chain distribution centers, airport luggage systems, and custom-order manufacturing facilities, face significant challenges in responding to requests for inventory items. As inventory systems grow, the challenges of simultaneously completing a large number of packing, storing, sorting, retrieving, and other inventory-related tasks become non-trivial. In inventory systems tasked with responding to large numbers of diverse inventory requests, inefficient utilization of system resources, including space, equipment, and manpower, can result in lower throughput, unacceptably long response times, an ever-increasing backlog of unfinished tasks, and, in general, poor system performance. Additionally, expanding or reducing the size or capabilities of many inventory systems requires significant changes to existing infrastructure and equipment. As a result, the cost of incremental changes to capacity or functionality may be prohibitively expensive, limiting the ability of the system to accommodate fluctuations in system throughput.
Inventory systems can enhance throughput by efficiently using space and by employing automation, including robotic means to lift, transport, and place inventory. One heretofore significant drawback in such automation has been the difficulty that robotic inventory handlers have in locating objects co-located in containers. Furthermore, robotic inventory handlers can be expensive and time-consuming to implement, unlike a human workforce, which can be allocated according to need. For that reason, conventional inventory systems continue to utilize personnel for many inventory manipulation tasks, even though human intervention tends to increase costs and decrease speed throughout any automated system. For these reasons, increased flexibility and performance are desired in robotic inventory handling systems.